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feat: add readme .gitignore

parent 37faa701
Showing with 12 additions and 162 deletions
.gitignore 0 → 100644
.idea
__pycache__
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# Default ignored files
/shelf/
/workspace.xml
# Editor-based HTTP Client requests
/httpRequests/
# Datasource local storage ignored files
/dataSources/
/dataSources.local.xml
.idea/auto-prompt.iml deleted 100644 → 0
<?xml version="1.0" encoding="UTF-8"?>
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<orderEntry type="inheritedJdk" />
<orderEntry type="sourceFolder" forTests="false" />
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</module>
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.idea/inspectionProfiles/Project_Default.xml deleted 100644 → 0
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.idea/inspectionProfiles/profiles_settings.xml deleted 100644 → 0
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.idea/misc.xml deleted 100644 → 0
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\ No newline at end of file
.idea/modules.xml deleted 100644 → 0
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectModuleManager">
<modules>
<module fileurl="file://$PROJECT_DIR$/.idea/auto-prompt.iml" filepath="$PROJECT_DIR$/.idea/auto-prompt.iml" />
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README.md 0 → 100644
# auto-prompt
自动调整提示词
\ No newline at end of file
logs/2025-09-09_11-31-39.jsonl
...@@ -30,3 +30,7 @@ ...@@ -30,3 +30,7 @@
{"name": "textgrad", "msg": "_backward_through_string_fn prompt", "args": "()", "levelname": "INFO", "levelno": "20", "pathname": "C:\\Users\\chenran\\AppData\\Roaming\\Python\\Python312\\site-packages\\textgrad\\autograd\\string_based_ops.py", "filename": "string_based_ops.py", "module": "string_based_ops", "exc_info": "None", "exc_text": "None", "stack_info": "None", "lineno": "179", "funcName": "_backward_through_string_fn_base", "created": "1757388924.0729938", "msecs": "72.0", "relativeCreated": "225095.61920166016", "thread": "88636", "threadName": "MainThread", "processName": "MainProcess", "process": "83832", "taskName": "None", "_backward_through_string_fn": "You will give feedback to a variable with the following role: <ROLE> response from the language model </ROLE>. Here is an evaluation of the variable using a string-based function:\n\nFunction purpose: The runtime of string-based function that checks if the prediction is correct.\n\n<INPUTS_TO_FUNCTION> **Prediction(role: response from the language model)**: \n\n```json\n[\n {\n (...) \"suggest\": \"建议补充'双方协商'条款，并明确合同取消/变更需经协商一致。若涉及退货条款，需将违约金比例调整至80%以上。\"\n }\n]\n```\n\n**Ground truth answer(role: correct answer for the query)**: 不合格 </INPUTS_TO_FUNCTION>\n\n<OUTPUT_OF_FUNCTION> 正确 </OUTPUT_OF_FUNCTION>\n\n<OBJECTIVE_FUNCTION>Your goal is to give feedback and criticism to the variable given the above evaluation output. Our only goal is to improve the above metric, and nothing else. </OBJECTIVE_FUNCTION>\n\nWe are interested in giving feedback to the response from the language model for this conversation. Specifically, give feedback to the following span of text:\n\n<VARIABLE> \n\n```json\n[\n {\n (...) \"suggest\": \"建议补充'双方协商'条款，并明确合同取消/变更需经协商一致。若涉及退货条款，需将违约金比例调整至80%以上。\"\n }\n]\n``` </VARIABLE>\n\nGiven the above history, describe how the response from the language model could be improved to improve the <OBJECTIVE_FUNCTION>. Be very creative, critical, and intelligent.\n\n", "message": "_backward_through_string_fn prompt"} {"name": "textgrad", "msg": "_backward_through_string_fn prompt", "args": "()", "levelname": "INFO", "levelno": "20", "pathname": "C:\\Users\\chenran\\AppData\\Roaming\\Python\\Python312\\site-packages\\textgrad\\autograd\\string_based_ops.py", "filename": "string_based_ops.py", "module": "string_based_ops", "exc_info": "None", "exc_text": "None", "stack_info": "None", "lineno": "179", "funcName": "_backward_through_string_fn_base", "created": "1757388924.0729938", "msecs": "72.0", "relativeCreated": "225095.61920166016", "thread": "88636", "threadName": "MainThread", "processName": "MainProcess", "process": "83832", "taskName": "None", "_backward_through_string_fn": "You will give feedback to a variable with the following role: <ROLE> response from the language model </ROLE>. Here is an evaluation of the variable using a string-based function:\n\nFunction purpose: The runtime of string-based function that checks if the prediction is correct.\n\n<INPUTS_TO_FUNCTION> **Prediction(role: response from the language model)**: \n\n```json\n[\n {\n (...) \"suggest\": \"建议补充'双方协商'条款，并明确合同取消/变更需经协商一致。若涉及退货条款，需将违约金比例调整至80%以上。\"\n }\n]\n```\n\n**Ground truth answer(role: correct answer for the query)**: 不合格 </INPUTS_TO_FUNCTION>\n\n<OUTPUT_OF_FUNCTION> 正确 </OUTPUT_OF_FUNCTION>\n\n<OBJECTIVE_FUNCTION>Your goal is to give feedback and criticism to the variable given the above evaluation output. Our only goal is to improve the above metric, and nothing else. </OBJECTIVE_FUNCTION>\n\nWe are interested in giving feedback to the response from the language model for this conversation. Specifically, give feedback to the following span of text:\n\n<VARIABLE> \n\n```json\n[\n {\n (...) \"suggest\": \"建议补充'双方协商'条款，并明确合同取消/变更需经协商一致。若涉及退货条款，需将违约金比例调整至80%以上。\"\n }\n]\n``` </VARIABLE>\n\nGiven the above history, describe how the response from the language model could be improved to improve the <OBJECTIVE_FUNCTION>. Be very creative, critical, and intelligent.\n\n", "message": "_backward_through_string_fn prompt"}
{"name": "textgrad", "msg": "_backward_through_string_fn gradient", "args": "()", "levelname": "INFO", "levelno": "20", "pathname": "C:\\Users\\chenran\\AppData\\Roaming\\Python\\Python312\\site-packages\\textgrad\\autograd\\string_based_ops.py", "filename": "string_based_ops.py", "module": "string_based_ops", "exc_info": "None", "exc_text": "None", "stack_info": "None", "lineno": "181", "funcName": "_backward_through_string_fn_base", "created": "1757388993.028691", "msecs": "28.0", "relativeCreated": "294051.3164997101", "thread": "88636", "threadName": "MainThread", "processName": "MainProcess", "process": "83832", "taskName": "None", "_backward_through_string_fn": "\n\nThe response from the language model is technically correct in its suggestion but could be improved by addressing the **root cause of the contract's unqualification** and aligning more explicitly with the ground truth label \"不合格\". Here’s how to refine it:\n\n1. **Link Suggestions to the Ground Truth**: \n The model’s suggestion focuses on procedural terms (\"双方协商\") and breach penalties (80%+), but the ground truth \"不合格\" likely indicates a **fundamental flaw** in the contract’s structure or compliance. The response should explicitly connect the suggested changes to the reasons the contract is unqualified (e.g., missing mandatory clauses, non-compliance with legal standards, or ambiguous terms). For example: \n *\"建议补充'双方协商'条款，以解决合同变更流程不明确的问题（当前合同因缺乏协商机制导致不合格）。同时，将退货条款的违约金比例调整至80%以上，以符合行业合规标准。\"*\n\n2. **Prioritize Critical Fixes Over General Advice**: \n The current suggestion is generic. Instead, identify **specific clauses or legal requirements** the contract violates. For instance: \n *\"当前合同因未包含[具体法律条款名称]而不合格。建议补充[具体条款]，并明确[具体义务]，以确保合规性。\"*\n\n3. **Quantify or Contextualize the 80% Breach Penalty**: \n The 80% threshold may be arbitrary. Clarify why this number is appropriate (e.g., legal precedent, industry benchmarks) to strengthen the suggestion’s validity. \n *\"根据[相关法规/案例]，违约金比例需不低于80%以保障双方权益，建议调整至80%以上。\"*\n\n4. **Address Structural Deficiencies**: \n If the contract is \"不合格\" due to missing sections (e.g., dispute resolution, termination conditions), the response should explicitly name these gaps and propose targeted fixes. For example: \n *\"建议补充[争议解决条款]，并明确[具体流程]，以解决当前合同因缺乏争议处理机制而不合格的问题。\"*\n\n5. **Use Clearer Language for Legal Precision**: \n Replace vague terms like \"协商一致\" with legally binding phrasing (e.g., \"书面协议\") to avoid ambiguity. \n *\"合同取消/变更需经双方书面协议，以确保法律效力。\"*\n\nBy anchoring the suggestions to the **specific reasons for the \"不合格\" label** and providing **actionable, legally grounded fixes**, the response would better align with the objective of improving the evaluation metric. This approach ensures the model’s output is not only technically correct but also directly addresses the root cause of the contract’s unqualification.", "message": "_backward_through_string_fn gradient"} {"name": "textgrad", "msg": "_backward_through_string_fn gradient", "args": "()", "levelname": "INFO", "levelno": "20", "pathname": "C:\\Users\\chenran\\AppData\\Roaming\\Python\\Python312\\site-packages\\textgrad\\autograd\\string_based_ops.py", "filename": "string_based_ops.py", "module": "string_based_ops", "exc_info": "None", "exc_text": "None", "stack_info": "None", "lineno": "181", "funcName": "_backward_through_string_fn_base", "created": "1757388993.028691", "msecs": "28.0", "relativeCreated": "294051.3164997101", "thread": "88636", "threadName": "MainThread", "processName": "MainProcess", "process": "83832", "taskName": "None", "_backward_through_string_fn": "\n\nThe response from the language model is technically correct in its suggestion but could be improved by addressing the **root cause of the contract's unqualification** and aligning more explicitly with the ground truth label \"不合格\". Here’s how to refine it:\n\n1. **Link Suggestions to the Ground Truth**: \n The model’s suggestion focuses on procedural terms (\"双方协商\") and breach penalties (80%+), but the ground truth \"不合格\" likely indicates a **fundamental flaw** in the contract’s structure or compliance. The response should explicitly connect the suggested changes to the reasons the contract is unqualified (e.g., missing mandatory clauses, non-compliance with legal standards, or ambiguous terms). For example: \n *\"建议补充'双方协商'条款，以解决合同变更流程不明确的问题（当前合同因缺乏协商机制导致不合格）。同时，将退货条款的违约金比例调整至80%以上，以符合行业合规标准。\"*\n\n2. **Prioritize Critical Fixes Over General Advice**: \n The current suggestion is generic. Instead, identify **specific clauses or legal requirements** the contract violates. For instance: \n *\"当前合同因未包含[具体法律条款名称]而不合格。建议补充[具体条款]，并明确[具体义务]，以确保合规性。\"*\n\n3. **Quantify or Contextualize the 80% Breach Penalty**: \n The 80% threshold may be arbitrary. Clarify why this number is appropriate (e.g., legal precedent, industry benchmarks) to strengthen the suggestion’s validity. \n *\"根据[相关法规/案例]，违约金比例需不低于80%以保障双方权益，建议调整至80%以上。\"*\n\n4. **Address Structural Deficiencies**: \n If the contract is \"不合格\" due to missing sections (e.g., dispute resolution, termination conditions), the response should explicitly name these gaps and propose targeted fixes. For example: \n *\"建议补充[争议解决条款]，并明确[具体流程]，以解决当前合同因缺乏争议处理机制而不合格的问题。\"*\n\n5. **Use Clearer Language for Legal Precision**: \n Replace vague terms like \"协商一致\" with legally binding phrasing (e.g., \"书面协议\") to avoid ambiguity. \n *\"合同取消/变更需经双方书面协议，以确保法律效力。\"*\n\nBy anchoring the suggestions to the **specific reasons for the \"不合格\" label** and providing **actionable, legally grounded fixes**, the response would better align with the objective of improving the evaluation metric. This approach ensures the model’s output is not only technically correct but also directly addresses the root cause of the contract’s unqualification.", "message": "_backward_through_string_fn gradient"}
{"name": "textgrad", "msg": "_backward_through_llm prompt", "args": "()", "levelname": "INFO", "levelno": "20", "pathname": "C:\\Users\\chenran\\AppData\\Roaming\\Python\\Python312\\site-packages\\textgrad\\autograd\\llm_ops.py", "filename": "llm_ops.py", "module": "llm_ops", "exc_info": "None", "exc_text": "None", "stack_info": "None", "lineno": "148", "funcName": "_backward_through_llm_chain", "created": "1757388993.028691", "msecs": "28.0", "relativeCreated": "294051.3164997101", "thread": "88636", "threadName": "MainThread", "processName": "MainProcess", "process": "83832", "taskName": "None", "_backward_through_llm": "You will give feedback to a variable with the following role: <ROLE> system prompt to guide the LLM's reasoning strategy for accurate responses </ROLE>. Here is a conversation with a language model (LM):\n\n<LM_SYSTEM_PROMPT> 你是乙方（供方、卖方）法律部门的合同审查助手\n# 审查要点\n1）提取涉及到句子的主体为甲方/买方/需方，句子内容为“合同变更/取消”、“退货”相关的句子，没有则返回不涉及\n2）句子明确提及了“双方协商”，审查合格\n3）句子没有明确提及“合同变更/取消”、“中途退货”所需要承担的责任，审查不合格\n3）“合同变更/取消”相关的句子，没有提及违约金额，审查不合格\n4）“退货”相关的句子，违约金的比例低于80%，审查不合格\n\n# 不合格建议\n1、提醒用户不合规的变更取消责任\n\n# 审查约束\n- 输出包括审查的原文、详情、结果、建议\n- 审查结果为合格/不合格/不涉及，合格/不涉及的审查结果无需输出建议\n- 审查原文严格提取关键、无省略、无篡改的原文内容\n- 结果以JSON数组的格式返回,例如```json [{\"original_text\":\"xx\",\"details\":\"xx\",\"result\":\"xx\",\"suggest\":\"xx\"}]```\n依据审查要点，遵循约束，完成合同审查，提供审查建议，一步步仔细思考。 </LM_SYSTEM_PROMPT>\n\n<LM_INPUT> 8.1.2.2 2.13.3.5 甲方有权取消该笔订单/合同，乙方需支付违约基数30%的违约金。 </LM_INPUT>\n\n<LM_OUTPUT> \n\n```json\n[\n {\n \"original_text\": \"甲方有权取消该笔订单/合同，乙方需支付违约基数30%的违约金。\",\n \"details\": \"条款涉及甲方单方取消合同的权利，且明确乙方需支付违约基数30%的违约金。但未提及'双方协商'，且违约金比例（30%）未达到退货条款要求的80%标准。\",\n \"result\": \"不合格\",\n \"suggest\": \"建议补充'双方协商'条款，并明确合同取消/变更需经协商一致。若涉及退货条款，需将违约金比例调整至80%以上。\"\n }\n]\n``` </LM_OUTPUT>\n\nThis conversation is part of a larger system. The <LM_OUTPUT> was later used as response from the language model.\n\n<OBJECTIVE_FUNCTION>Your goal is to give feedback to the variable to address the following feedback on the LM_OUTPUT: \n\nThe response from the language model is technically correct in its suggestion but could be improved by addressing the **root cause of the contract's unqualification** and aligning more explicitly with the ground truth label \"不合格\". Here’s how to refine it:\n\n1. **Link Suggestions to the Ground Truth**: \n The model’s suggestion focuses on procedural terms (\"双方协商\") and breach penalties (80%+), but the ground truth \"不合格\" likely indicates a **fundamental flaw** in the contract’s structure or compliance. The response should explicitly connect the suggested changes to the reasons the contract is unqualified (e.g., missing mandatory clauses, non-compliance with legal standards, or ambiguous terms). For example: \n *\"建议补充'双方协商'条款，以解决合同变更流程不明确的问题（当前合同因缺乏协商机制导致不合格）。同时，将退货条款的违约金比例调整至80%以上，以符合行业合规标准。\"*\n\n2. **Prioritize Critical Fixes Over General Advice**: \n The current suggestion is generic. Instead, identify **specific clauses or legal requirements** the contract violates. For instance: \n *\"当前合同因未包含[具体法律条款名称]而不合格。建议补充[具体条款]，并明确[具体义务]，以确保合规性。\"*\n\n3. **Quantify or Contextualize the 80% Breach Penalty**: \n The 80% threshold may be arbitrary. Clarify why this number is appropriate (e.g., legal precedent, industry benchmarks) to strengthen the suggestion’s validity. \n *\"根据[相关法规/案例]，违约金比例需不低于80%以保障双方权益，建议调整至80%以上。\"*\n\n4. **Address Structural Deficiencies**: \n If the contract is \"不合格\" due to missing sections (e.g., dispute resolution, termination conditions), the response should explicitly name these gaps and propose targeted fixes. For example: \n *\"建议补充[争议解决条款]，并明确[具体流程]，以解决当前合同因缺乏争议处理机制而不合格的问题。\"*\n\n5. **Use Clearer Language for Legal Precision**: \n Replace vague terms like \"协商一致\" with legally binding phrasing (e.g., \"书面协议\") to avoid ambiguity. \n *\"合同取消/变更需经双方书面协议，以确保法律效力。\"*\n\nBy anchoring the suggestions to the **specific reasons for the \"不合格\" label** and providing **actionable, legally grounded fixes**, the response would better align with the objective of improving the evaluation metric. This approach ensures the model’s output is not only technically correct but also directly addresses the root cause of the contract’s unqualification. </OBJECTIVE_FUNCTION>\n\nWe are interested in giving feedback to the system prompt to guide the LLM's reasoning strategy for accurate responses for this conversation. Specifically, give feedback to the following span of text:\n\n<VARIABLE> 你是乙方（供方、卖方）法律部门的合同审查助手\n# 审查要点\n1）提取涉及到句子的主体为甲方/买方/需方，句子内容为“合同变更/取消”、“退货”相关的句子，没有则返回不涉及\n2）句子明确提及了“双方协商”，审查合格\n3）句子没有明确提及“合同变更/取消”、“中途退货”所需要承担的责任，审查不合格\n3）“合同变更/取消”相关的句子，没有提及违约金额，审查不合格\n4）“退货”相关的句子，违约金的比例低于80%，审查不合格\n\n# 不合格建议\n1、提醒用户不合规的变更取消责任\n\n# 审查约束\n- 输出包括审查的原文、详情、结果、建议\n- 审查结果为合格/不合格/不涉及，合格/不涉及的审查结果无需输出建议\n- 审查原文严格提取关键、无省略、无篡改的原文内容\n- 结果以JSON数组的格式返回,例如```json [{\"original_text\":\"xx\",\"details\":\"xx\",\"result\":\"xx\",\"suggest\":\"xx\"}]```\n依据审查要点，遵循约束，完成合同审查，提供审查建议，一步步仔细思考。 </VARIABLE>\n\nGiven the above history, describe how the system prompt to guide the LLM's reasoning strategy for accurate responses could be improved to improve the <OBJECTIVE_FUNCTION>. Be very creative, critical, and intelligent.\n\n", "message": "_backward_through_llm prompt"} {"name": "textgrad", "msg": "_backward_through_llm prompt", "args": "()", "levelname": "INFO", "levelno": "20", "pathname": "C:\\Users\\chenran\\AppData\\Roaming\\Python\\Python312\\site-packages\\textgrad\\autograd\\llm_ops.py", "filename": "llm_ops.py", "module": "llm_ops", "exc_info": "None", "exc_text": "None", "stack_info": "None", "lineno": "148", "funcName": "_backward_through_llm_chain", "created": "1757388993.028691", "msecs": "28.0", "relativeCreated": "294051.3164997101", "thread": "88636", "threadName": "MainThread", "processName": "MainProcess", "process": "83832", "taskName": "None", "_backward_through_llm": "You will give feedback to a variable with the following role: <ROLE> system prompt to guide the LLM's reasoning strategy for accurate responses </ROLE>. Here is a conversation with a language model (LM):\n\n<LM_SYSTEM_PROMPT> 你是乙方（供方、卖方）法律部门的合同审查助手\n# 审查要点\n1）提取涉及到句子的主体为甲方/买方/需方，句子内容为“合同变更/取消”、“退货”相关的句子，没有则返回不涉及\n2）句子明确提及了“双方协商”，审查合格\n3）句子没有明确提及“合同变更/取消”、“中途退货”所需要承担的责任，审查不合格\n3）“合同变更/取消”相关的句子，没有提及违约金额，审查不合格\n4）“退货”相关的句子，违约金的比例低于80%，审查不合格\n\n# 不合格建议\n1、提醒用户不合规的变更取消责任\n\n# 审查约束\n- 输出包括审查的原文、详情、结果、建议\n- 审查结果为合格/不合格/不涉及，合格/不涉及的审查结果无需输出建议\n- 审查原文严格提取关键、无省略、无篡改的原文内容\n- 结果以JSON数组的格式返回,例如```json [{\"original_text\":\"xx\",\"details\":\"xx\",\"result\":\"xx\",\"suggest\":\"xx\"}]```\n依据审查要点，遵循约束，完成合同审查，提供审查建议，一步步仔细思考。 </LM_SYSTEM_PROMPT>\n\n<LM_INPUT> 8.1.2.2 2.13.3.5 甲方有权取消该笔订单/合同，乙方需支付违约基数30%的违约金。 </LM_INPUT>\n\n<LM_OUTPUT> \n\n```json\n[\n {\n \"original_text\": \"甲方有权取消该笔订单/合同，乙方需支付违约基数30%的违约金。\",\n \"details\": \"条款涉及甲方单方取消合同的权利，且明确乙方需支付违约基数30%的违约金。但未提及'双方协商'，且违约金比例（30%）未达到退货条款要求的80%标准。\",\n \"result\": \"不合格\",\n \"suggest\": \"建议补充'双方协商'条款，并明确合同取消/变更需经协商一致。若涉及退货条款，需将违约金比例调整至80%以上。\"\n }\n]\n``` </LM_OUTPUT>\n\nThis conversation is part of a larger system. The <LM_OUTPUT> was later used as response from the language model.\n\n<OBJECTIVE_FUNCTION>Your goal is to give feedback to the variable to address the following feedback on the LM_OUTPUT: \n\nThe response from the language model is technically correct in its suggestion but could be improved by addressing the **root cause of the contract's unqualification** and aligning more explicitly with the ground truth label \"不合格\". Here’s how to refine it:\n\n1. **Link Suggestions to the Ground Truth**: \n The model’s suggestion focuses on procedural terms (\"双方协商\") and breach penalties (80%+), but the ground truth \"不合格\" likely indicates a **fundamental flaw** in the contract’s structure or compliance. The response should explicitly connect the suggested changes to the reasons the contract is unqualified (e.g., missing mandatory clauses, non-compliance with legal standards, or ambiguous terms). For example: \n *\"建议补充'双方协商'条款，以解决合同变更流程不明确的问题（当前合同因缺乏协商机制导致不合格）。同时，将退货条款的违约金比例调整至80%以上，以符合行业合规标准。\"*\n\n2. **Prioritize Critical Fixes Over General Advice**: \n The current suggestion is generic. Instead, identify **specific clauses or legal requirements** the contract violates. For instance: \n *\"当前合同因未包含[具体法律条款名称]而不合格。建议补充[具体条款]，并明确[具体义务]，以确保合规性。\"*\n\n3. **Quantify or Contextualize the 80% Breach Penalty**: \n The 80% threshold may be arbitrary. Clarify why this number is appropriate (e.g., legal precedent, industry benchmarks) to strengthen the suggestion’s validity. \n *\"根据[相关法规/案例]，违约金比例需不低于80%以保障双方权益，建议调整至80%以上。\"*\n\n4. **Address Structural Deficiencies**: \n If the contract is \"不合格\" due to missing sections (e.g., dispute resolution, termination conditions), the response should explicitly name these gaps and propose targeted fixes. For example: \n *\"建议补充[争议解决条款]，并明确[具体流程]，以解决当前合同因缺乏争议处理机制而不合格的问题。\"*\n\n5. **Use Clearer Language for Legal Precision**: \n Replace vague terms like \"协商一致\" with legally binding phrasing (e.g., \"书面协议\") to avoid ambiguity. \n *\"合同取消/变更需经双方书面协议，以确保法律效力。\"*\n\nBy anchoring the suggestions to the **specific reasons for the \"不合格\" label** and providing **actionable, legally grounded fixes**, the response would better align with the objective of improving the evaluation metric. This approach ensures the model’s output is not only technically correct but also directly addresses the root cause of the contract’s unqualification. </OBJECTIVE_FUNCTION>\n\nWe are interested in giving feedback to the system prompt to guide the LLM's reasoning strategy for accurate responses for this conversation. Specifically, give feedback to the following span of text:\n\n<VARIABLE> 你是乙方（供方、卖方）法律部门的合同审查助手\n# 审查要点\n1）提取涉及到句子的主体为甲方/买方/需方，句子内容为“合同变更/取消”、“退货”相关的句子，没有则返回不涉及\n2）句子明确提及了“双方协商”，审查合格\n3）句子没有明确提及“合同变更/取消”、“中途退货”所需要承担的责任，审查不合格\n3）“合同变更/取消”相关的句子，没有提及违约金额，审查不合格\n4）“退货”相关的句子，违约金的比例低于80%，审查不合格\n\n# 不合格建议\n1、提醒用户不合规的变更取消责任\n\n# 审查约束\n- 输出包括审查的原文、详情、结果、建议\n- 审查结果为合格/不合格/不涉及，合格/不涉及的审查结果无需输出建议\n- 审查原文严格提取关键、无省略、无篡改的原文内容\n- 结果以JSON数组的格式返回,例如```json [{\"original_text\":\"xx\",\"details\":\"xx\",\"result\":\"xx\",\"suggest\":\"xx\"}]```\n依据审查要点，遵循约束，完成合同审查，提供审查建议，一步步仔细思考。 </VARIABLE>\n\nGiven the above history, describe how the system prompt to guide the LLM's reasoning strategy for accurate responses could be improved to improve the <OBJECTIVE_FUNCTION>. Be very creative, critical, and intelligent.\n\n", "message": "_backward_through_llm prompt"}
{"name": "textgrad", "msg": "_backward_through_llm gradient", "args": "()", "levelname": "INFO", "levelno": "20", "pathname": "C:\\Users\\chenran\\AppData\\Roaming\\Python\\Python312\\site-packages\\textgrad\\autograd\\llm_ops.py", "filename": "llm_ops.py", "module": "llm_ops", "exc_info": "None", "exc_text": "None", "stack_info": "None", "lineno": "150", "funcName": "_backward_through_llm_chain", "created": "1757389074.937614", "msecs": "937.0", "relativeCreated": "375960.2394104004", "thread": "88636", "threadName": "MainThread", "processName": "MainProcess", "process": "83832", "taskName": "None", "_backward_through_llm": "\n\nTo improve the system prompt for the LLM to align with the <OBJECTIVE_FUNCTION> and address the root causes of the \"不合格\" label, the following **strategic revisions** are recommended:\n\n---\n\n### **1. Anchor Review Criteria to Legal Standards and Root Causes**\n**Current Issue**: The review criteria are procedural (e.g., \"双方协商\") but lack explicit ties to legal requirements or the *why* behind the \"不合格\" label. \n**Feedback**: \n- **Add a \"Legal Basis\" Clause**: \n Modify the review points to require the model to **explicitly reference legal statutes, case law, or industry benchmarks** when flagging issues. For example: \n > *\"3) 若句子未明确提及‘合同变更/取消’所需承担的责任，且未符合《民法典》第563条关于合同解除的法定条件，审查不合格。\"* \n This forces the model to link \"不合格\" to specific legal gaps. \n\n- **Introduce a \"Root Cause\" Field**: \n Add a new review constraint: \n > *\"在详情中必须说明合同不合格的根本原因（如：缺乏协商机制、违约金比例未达法定标准、未明确争议解决条款等）。\"* \n This ensures the model doesn’t just list symptoms but diagnoses the problem.\n\n---\n\n### **2. Prioritize Critical Fixes with Specific Legal Clauses**\n**Current Issue**: The \"不合格建议\" is generic (e.g., \"补充‘双方协商’条款\") and doesn’t name specific legal requirements. \n**Feedback**: \n- **Mandate Clause-Specific Suggestions**: \n Update the \"不合格建议\" section to require the model to: \n > *\"1、建议补充《民法典》第563条规定的合同解除协商条款，并明确违约金计算方式（如：违约基数的80%）。\"* \n This ties the fix to a concrete legal provision and quantifies the required change. \n\n- **Add a \"Critical Clause Checklist\"**: \n Include a new review point: \n > *\"5) 若合同未包含以下任一法定条款（如：争议解决、不可抗力、违约责任），审查不合格。\"* \n This ensures the model identifies structural deficiencies (e.g., missing dispute resolution clauses) as root causes.\n\n---\n\n### **3. Quantify the 80% Threshold with Legal/Industry Context**\n**Current Issue**: The 80% breach penalty threshold is arbitrary without justification. \n**Feedback**: \n- **Embed Contextual Justification**: \n Add a new review point: \n > *\"4) ‘退货’相关条款的违约金比例低于80%，且未符合《合同法司法解释》第29条关于违约金合理性的规定，审查不合格。\"* \n This explains *why* 80% is the benchmark (e.g., legal precedent) and strengthens the suggestion’s validity. \n\n- **Require Causal Linking in Suggestions**: \n Update the \"suggest\" constraint: \n > *\"建议必须说明调整违约金比例至80%以上的原因（如：符合《XX法》第X条或行业惯例）。\"* \n Example: \n > *\"根据《XX行业标准》，违约金比例需不低于80%以平衡双方权益，建议调整至80%以上。\"*\n\n---\n\n### **4. Use Legally Binding Language for Precision**\n**Current Issue**: Vague terms like \"协商一致\" lack enforceability. \n**Feedback**: \n- **Replace Ambiguous Phrases**: \n Add a new constraint: \n > *\"建议中必须使用具有法律效力的术语（如：‘书面协议’、‘仲裁条款’），避免使用‘协商一致’等模糊表述。\"* \n Example: \n > *\"合同变更需经双方书面协议，以确保法律效力（《民法典》第490条）。\"*\n\n---\n\n### **5. Structure Output to Reflect Root Cause-Driven Fixes**\n**Current Issue**: The JSON output is flat and doesn’t prioritize critical issues. \n**Feedback**: \n- **Add a \"Root Cause Category\" Field**: \n Modify the JSON structure to include: \n ```json\n {\n \"original_text\": \"...\",\n \"details\": \"条款违反《民法典》第563条，因缺乏协商解除机制导致不合格。\",\n \"result\": \"不合格\",\n \"suggest\": \"建议补充书面协商解除条款，并明确违约金比例不低于80%（依据《XX法》第X条）。\",\n \"root_cause\": \"缺乏法定协商机制\"\n }\n ``` \n This explicitly ties the suggestion to the root cause and legal basis.\n\n---\n\n### **6. Add a \"Structural Deficiency\" Review Point**\n**Current Issue**: The prompt ignores missing contract sections (e.g., dispute resolution). \n**Feedback**: \n- **Flag Missing Structural Elements**: \n Add a new review point: \n > *\"6) 若合同未包含争议解决条款或违约责任章节，审查不合格。\"* \n Example suggestion: \n > *\"建议补充争议解决条款，明确通过XX仲裁委员会仲裁（《仲裁法》第5条），以解决当前合同因缺乏争议处理机制而不合格的问题。\"*\n\n---\n\n### **7. Train the Model to Prioritize Critical Fixes**\n**Current Issue**: The model offers generic advice (e.g., \"补充协商条款\") without prioritizing severity. \n**Feedback**: \n- **Add a \"Severity Ranking\" Constraint**: \n Update the output to include: \n > *\"在详情中按严重性排序问题（如：1. 缺乏协商机制；2. 违约金比例不足）。\"* \n Example: \n > *\"1. 根本问题：未包含法定协商", "message": "_backward_through_llm gradient"}
{"name": "textgrad", "msg": "TextualGradientDescent prompt for update", "args": "()", "levelname": "INFO", "levelno": "20", "pathname": "C:\\Users\\chenran\\AppData\\Roaming\\Python\\Python312\\site-packages\\textgrad\\optimizer\\optimizer.py", "filename": "optimizer.py", "module": "optimizer", "exc_info": "None", "exc_text": "None", "stack_info": "None", "lineno": "165", "funcName": "_update_prompt", "created": "1757389074.938824", "msecs": "938.0", "relativeCreated": "375961.44938468933", "thread": "88636", "threadName": "MainThread", "processName": "MainProcess", "process": "83832", "taskName": "None", "prompt": "Here is the role of the variable you will improve: <ROLE>system prompt to guide the LLM's reasoning strategy for accurate responses</ROLE>.\n\nThe variable is the text within the following span: <VARIABLE> 你是乙方（供方、卖方）法律部门的合同审查助手\n# 审查要点\n1）提取涉及到句子的主体为甲方/买方/需方，句子内容为“合同变更/取消”、“退货”相关的句子，没有则返回不涉及\n2）句子明确提及了“双方协商”，审查合格\n3）句子没有明确提及“合同变更/取消”、“中途退货”所需要承担的责任，审查不合格\n3）“合同变更/取消”相关的句子，没有提及违约金额，审查不合格\n4）“退货”相关的句子，违约金的比例低于80%，审查不合格\n\n# 不合格建议\n1、提醒用户不合规的变更取消责任\n\n# 审查约束\n- 输出包括审查的原文、详情、结果、建议\n- 审查结果为合格/不合格/不涉及，合格/不涉及的审查结果无需输出建议\n- 审查原文严格提取关键、无省略、无篡改的原文内容\n- 结果以JSON数组的格式返回,例如```json [{\"original_text\":\"xx\",\"details\":\"xx\",\"result\":\"xx\",\"suggest\":\"xx\"}]```\n依据审查要点，遵循约束，完成合同审查，提供审查建议，一步步仔细思考。 </VARIABLE>\n\nHere is the context and feedback we got for the variable:\n\n<CONTEXT>Here is a conversation:\n\n<CONVERSATION><LM_SYSTEM_PROMPT> 你是乙方（供方、卖方）法律部门的合同审查助手\n# 审查要点\n1）提取涉及到句子的主体为甲方/买方/需方，句子内容为“合同变更/取消”、“退货”相关的句子，没有则返回不涉及\n2）句子明确提及了“双方协商”，审查合格\n3）句子没有明确提及“合同变更/取消”、“中途退货”所需要承担的责任，审查不合格\n3）“合同变更/取消”相关的句子，没有提及违约金额，审查不合格\n4）“退货”相关的句子，违约金的比例低于80%，审查不合格\n\n# 不合格建议\n1、提醒用户不合规的变更取消责任\n\n# 审查约束\n- 输出包括审查的原文、详情、结果、建议\n- 审查结果为合格/不合格/不涉及，合格/不涉及的审查结果无需输出建议\n- 审查原文严格提取关键、无省略、无篡改的原文内容\n- 结果以JSON数组的格式返回,例如```json [{\"original_text\":\"xx\",\"details\":\"xx\",\"result\":\"xx\",\"suggest\":\"xx\"}]```\n依据审查要点，遵循约束，完成合同审查，提供审查建议，一步步仔细思考。 </LM_SYSTEM_PROMPT>\n\n<LM_INPUT> 8.1.2.2 要求乙方全额退还甲方已支付的预付款项。 </LM_INPUT>\n\n<LM_OUTPUT> \n\n```json\n[\n {\n \"original_text\": \"8.1.2.2 要求乙方全额退还甲方已支付的预付款项。\",\n \"details\": \"该条款涉及甲方要求退还预付款，但未明确提及'双方协商'，且未说明合同变更/取消或中途退货所需承担的责任，也未约定违约金额或违约金比例。\",\n \"result\": \"不合格\",\n \"suggest\": \"建议补充'双方协商一致'的表述，并明确合同变更/取消或中途退货时的责任承担方式及违约金比例（若涉及退货且违约金比例低于80%需调整）。\"\n }\n]\n``` </LM_OUTPUT>\n\n</CONVERSATION>\n\nThis conversation is potentially part of a larger system. The output is used as response from the language model\n\nHere is the feedback we got for system prompt to guide the LLM's reasoning strategy for accurate responses in the conversation:\n\n<FEEDBACK>\n\nTo address the false positive error in the LLM's response, the system prompt must be restructured to enforce **strict contextual relevance filtering** and **binary applicability checks** before applying review rules. Here's how to improve the system prompt:\n\n---\n\n### **1. Add a Pre-Validation Step for Scenario Applicability**\n**Current flaw**: The prompt assumes all input sentences inherently involve contract amendments/returns, leading to overgeneralization. \n**Fix**: Insert a mandatory initial step to validate whether the scenario involves the required elements. \n**Proposed addition to the system prompt**: \n```markdown\n# **Pre-Validation Check** \n- **First**, determine if the input sentence explicitly involves **contract amendment/cancellation** or **return conditions**. \n - If **no**, output `{\"result\": \"不涉及\"}` **without any suggestions**. \n - If **yes**, proceed to the review steps below. \n- **Rationale**: This prevents the model from applying rules to irrelevant scenarios (e.g., refund clauses unrelated to returns). \n```\n\n---\n\n### **2. Restructure Review Points to Prioritize Applicability**\n**Current flaw**: The review points are applied sequentially without first confirming the scenario's relevance. \n**Fix**: Reorder the review logic to first confirm applicability, then apply rules. \n**Proposed revision to the review points**: \n```markdown\n# **Revised Review Points** \n1. **Applicability Check**: \n - If the sentence does **not** involve \"合同变更/取消\" or \"退货\", return `{\"result\": \"不涉及\"}`. \n2. **主体与内容匹配**: \n - Extract sentences where the subject is 甲方/买方/需方 and the content relates to \"合同变更/取消\" or \"退货\". \n3. **合格条件**: \n - If the sentence explicitly includes \"双方协商\", mark as **合格**. \n4. **不合格条件**: \n - If the sentence lacks mention of responsibilities for \"合同变更/取消\" or \"中途退货\", mark as **不合格**. \n - If \"合同变更/取消\" is mentioned but no breach amount is specified, mark as **不合格**. \n - If \"退货\" is mentioned but the breach penalty is <80%, mark as **不合格**. \n```\n\n---\n\n### **3. Clarify the Role of \"双方协商\" as a Scenario-Dependent Clause**\n**Current flaw**: The model conflates \"双方协商\" as a universal legal principle with a scenario-specific requirement. \n**Fix**: Specify that \"双方协商\" is only a valid check if the scenario involves the required elements. \n**Proposed addition to the system prompt**: \n```markdown\n# **Legal Terminology Precision** \n- \"双方协商\" is a **valid requirement only if the scenario involves contract amendment/cancellation or return conditions. \n- If the scenario does **not** involve these elements, \"双方协商\" is irrelevant and should **not** be suggested. \n```\n\n---\n\n### **4. Enforce Zero Suggestions for \"不涉及\" and \"合格\" Cases**\n**Current flaw**: The model generates suggestions even when the result is \"不涉及\" or \"合格\". \n**Fix**: Explicitly prohibit suggestions in these cases. \n**Proposed revision to the review constraints**: \n```markdown\n# **Revised Review Constraints** \n- Output includes `original_text`, `details`, `result`, and **only** `suggest` if the result is **不合格**. \n- For `result`: \"合格\" or \"不涉及\" must **never** include a `suggest` field. \n- If the scenario is \"不涉及\", the `details` field must **only** explain why the scenario is irrelevant (e.g., \"该条款不涉及合同变更/取消或退货条款\"). \n```\n\n---\n\n### **5. Add Error Pattern Recognition for \"不涉及\" Scenarios**\n**Current flaw**: The model fails to recognize that suggesting clauses in \"不涉及\" cases is an error. \n**Fix**: Train the model to treat \"不涉及\" as a hard stop for suggestions. \n**Proposed addition to the system prompt**: \n```markdown\n# **Error Pattern Recognition** \n- If the ground truth is \"不涉及\", any suggestion of new clauses (e.g., \"双方协商\", breach penalties) is an **error**. \n- The model must **strictly avoid** introducing legal complexity in such cases. \n```\n\n---\n\n### **6. Example of Corrected Output for the Given Input**\nFor the input `8.1.2.2 要求乙方全额退还甲方已支付的预付款项。`, the revised system prompt would enforce: \n```json\n[\n {\n \"original_text\": \"8.1.2.2 要求乙方全额退还甲方已支付的预付款项。\",\n \"details\": \"该条款不涉及合同变更/取消或退货条款，仅涉及预付款退还。\",\n \"result\": \"不涉及\",\n \"suggest\": null\n</FEEDBACK>\n\n\nHere is a conversation:\n\n<CONVERSATION><LM_SYSTEM_PROMPT> 你是乙方（供方、卖方）法律部门的合同审查助手\n# 审查要点\n1）提取涉及到句子的主体为甲方/买方/需方，句子内容为“合同变更/取消”、“退货”相关的句子，没有则返回不涉及\n2）句子明确提及了“双方协商”，审查合格\n3）句子没有明确提及“合同变更/取消”、“中途退货”所需要承担的责任，审查不合格\n3）“合同变更/取消”相关的句子，没有提及违约金额，审查不合格\n4）“退货”相关的句子，违约金的比例低于80%，审查不合格\n\n# 不合格建议\n1、提醒用户不合规的变更取消责任\n\n# 审查约束\n- 输出包括审查的原文、详情、结果、建议\n- 审查结果为合格/不合格/不涉及，合格/不涉及的审查结果无需输出建议\n- 审查原文严格提取关键、无省略、无篡改的原文内容\n- 结果以JSON数组的格式返回,例如```json [{\"original_text\":\"xx\",\"details\":\"xx\",\"result\":\"xx\",\"suggest\":\"xx\"}]```\n依据审查要点，遵循约束，完成合同审查，提供审查建议，一步步仔细思考。 </LM_SYSTEM_PROMPT>\n\n<LM_INPUT> 8.1.2.6 向甲方支付违约基数30%的违约金。 </LM_INPUT>\n\n<LM_OUTPUT> \n\n```json\n[\n {\n \"original_text\": \"8.1.2.6 向甲方支付违约基数30%的违约金。\",\n \"details\": \"该条款未明确提及'合同变更/取消'或'退货'相关表述，且未说明违约金对应的违约行为类型（如中途退货、合同变更等）。违约金比例为30%，但缺乏具体适用场景的关联性描述。\",\n \"result\": \"不涉及\",\n \"suggest\": \"\"\n }\n]\n``` </LM_OUTPUT>\n\n</CONVERSATION>\n\nThis conversation is potentially part of a larger system. The output is used as response from the language model\n\nHere is the feedback we got for system prompt to guide the LLM's reasoning strategy for accurate responses in the conversation:\n\n<FEEDBACK>\n\nTo address the objective function's feedback and ensure the \"suggest\" field explicitly reflects \"不涉及\" when applicable, the system prompt requires the following strategic revisions:\n\n---\n\n### **1. Clarify the \"审查约束\" (Review Constraints)**\n**Current issue**: The constraint states *\"合格/不涉及的审查结果无需输出建议\"*, which ambiguously implies both \"合格\" and \"不涉及\" should omit the \"suggest\" field. This leads to empty strings for \"不涉及\", violating the objective of semantic alignment. \n\n**Feedback**: \n- **Modify the constraint** to explicitly differentiate between \"合格\" and \"不涉及\": \n - *\"合格的审查结果无需输出建议，不涉及的审查结果需在'suggest'字段中明确标注'不涉及'\"* \n - This ensures \"不涉及\" is semantically encoded in the \"suggest\" field, while \"合格\" retains an empty string. \n- **Add a parsing robustness note**: \n - *\"避免使用空字符串表示'不涉及'，改用'不涉及'文本以减少下游系统歧义\"* \n\n---\n\n### **2. Update the Example in \"审查约束\"**\n**Current issue**: The example in the system prompt shows `\"suggest\":\"\"` for \"不涉及\", reinforcing the incorrect pattern. \n\n**Feedback**: \n- **Revise the example** to reflect the desired behavior: \n ```json\n {\"original_text\":\"xx\",\"details\":\"xx\",\"result\":\"不涉及\",\"suggest\":\"不涉及\"}\n ``` \n This directly demonstrates the expected output and trains the LLM to associate \"不涉及\" with the \"suggest\" field. \n\n---\n\n### **3. Add a \"不涉及\" Case to \"不合格建议\" (Non-Compliance Suggestions)**\n**Current issue**: The \"不合格建议\" section only includes a suggestion for non-compliant change/cancellation responsibilities, leaving no guidance for \"不涉及\" scenarios. \n\n**Feedback**: \n- **Introduce a new bullet point**: \n - *\"若审查结果为'不涉及'，需在'suggest'字段中明确标注'不涉及'以确保语义一致性\"* \n This explicitly ties the \"不涉及\" label to the \"suggest\" field, reinforcing the objective function's requirement. \n\n---\n\n### **4. Strengthen the \"审查要点\" (Review Criteria)**\n**Current issue**: The first review criterion states *\"没有则返回不涉及\"*, but does not specify how to handle the \"suggest\" field. \n\n**Feedback**: \n- **Add a sub-point under review criterion 1**: \n - *\"若返回'不涉及'，需在'suggest'字段中明确标注'不涉及'，以确保输出与审查结果完全对齐\"* \n This creates a direct link between the \"不涉及\" result and the \"suggest\" field, reducing ambiguity. \n\n---\n\n### **5. Add a \"语义一致性\" (Semantic Consistency) Section**\n**Current issue**: The system prompt lacks explicit guidance on aligning output fields with semantic labels. \n\n**Feedback**: \n- **Insert a new section**: \n ```markdown\n # 语义一致性要求\n - 所有输出字段（如'result'、'suggest'）必须与审查结果的语义完全一致。例如：\n - '不涉及'结果必须在'suggest'字段中显式标注'不涉及'\n - '合格'结果的'suggest'字段可为空字符串\n - 禁止使用空字符串表示非'合格'结果，除非明确允许\n ``` \n This codifies the objective function's requirement into the system prompt, ensuring the LLM prioritizes semantic clarity. \n\n---\n\n### **6. Test Case Inclusion**\n**Current issue**: The system prompt lacks examples for edge cases like \"不涉及\" with explicit \"suggest\" fields. \n\n**Feedback**: \n- **Add a test case to the system prompt**: \n ```markdown\n # 测试用例\n - 输入: \"合同条款与变更/取消或退货无关\"\n - 预期输出: {\"original_text\":\"xx\",\"details\":\"xx\",\"result\":\"不涉及\",\"suggest\":\"不涉及\"}\n ``` \n This trains the LLM to recognize and replicate the desired pattern. \n\n---\n\n### **Why This Works** \n1. **Semantic alignment**: By explicitly tying \"不涉及\" to the \"suggest\" field, the LLM learns to avoid relying on implicit assumptions (e.g., empty strings). \n2. **Parsing robustness**: Downstream systems can unambiguously interpret \"不涉及\" without error-handling logic for empty strings. \n3. **Training signal clarity**: Future iterations of the LLM will receive stronger gradient signals when \"suggest\" fields are semantically consistent with \"result\" fields. \n\nThese changes ensure the system prompt directly addresses the objective function's feedback while maintaining the original constraints and logic.</FEEDBACK>\n\n\nHere is a conversation:\n\n<CONVERSATION><LM_SYSTEM_PROMPT> 你是乙方（供方、卖方）法律部门的合同审查助手\n# 审查要点\n1）提取涉及到句子的主体为甲方/买方/需方，句子内容为“合同变更/取消”、“退货”相关的句子，没有则返回不涉及\n2）句子明确提及了“双方协商”，审查合格\n3）句子没有明确提及“合同变更/取消”、“中途退货”所需要承担的责任，审查不合格\n3）“合同变更/取消”相关的句子，没有提及违约金额，审查不合格\n4）“退货”相关的句子，违约金的比例低于80%，审查不合格\n\n# 不合格建议\n1、提醒用户不合规的变更取消责任\n\n# 审查约束\n- 输出包括审查的原文、详情、结果、建议\n- 审查结果为合格/不合格/不涉及，合格/不涉及的审查结果无需输出建议\n- 审查原文严格提取关键、无省略、无篡改的原文内容\n- 结果以JSON数组的格式返回,例如```json [{\"original_text\":\"xx\",\"details\":\"xx\",\"result\":\"xx\",\"suggest\":\"xx\"}]```\n依据审查要点，遵循约束，完成合同审查，提供审查建议，一步步仔细思考。 </LM_SYSTEM_PROMPT>\n\n<LM_INPUT> 8.1.2.2 2.13.3.5 甲方有权取消该笔订单/合同，乙方需支付违约基数30%的违约金。 </LM_INPUT>\n\n<LM_OUTPUT> \n\n```json\n[\n {\n \"original_text\": \"甲方有权取消该笔订单/合同，乙方需支付违约基数30%的违约金。\",\n \"details\": \"条款涉及甲方单方取消合同的权利，且明确乙方需支付违约基数30%的违约金。但未提及'双方协商'，且违约金比例（30%）未达到退货条款要求的80%标准。\",\n \"result\": \"不合格\",\n \"suggest\": \"建议补充'双方协商'条款，并明确合同取消/变更需经协商一致。若涉及退货条款，需将违约金比例调整至80%以上。\"\n }\n]\n``` </LM_OUTPUT>\n\n</CONVERSATION>\n\nThis conversation is potentially part of a larger system. The output is used as response from the language model\n\nHere is the feedback we got for system prompt to guide the LLM's reasoning strategy for accurate responses in the conversation:\n\n<FEEDBACK>\n\nTo improve the system prompt for the LLM to align with the <OBJECTIVE_FUNCTION> and address the root causes of the \"不合格\" label, the following **strategic revisions** are recommended:\n\n---\n\n### **1. Anchor Review Criteria to Legal Standards and Root Causes**\n**Current Issue**: The review criteria are procedural (e.g., \"双方协商\") but lack explicit ties to legal requirements or the *why* behind the \"不合格\" label. \n**Feedback**: \n- **Add a \"Legal Basis\" Clause**: \n Modify the review points to require the model to **explicitly reference legal statutes, case law, or industry benchmarks** when flagging issues. For example: \n > *\"3) 若句子未明确提及‘合同变更/取消’所需承担的责任，且未符合《民法典》第563条关于合同解除的法定条件，审查不合格。\"* \n This forces the model to link \"不合格\" to specific legal gaps. \n\n- **Introduce a \"Root Cause\" Field**: \n Add a new review constraint: \n > *\"在详情中必须说明合同不合格的根本原因（如：缺乏协商机制、违约金比例未达法定标准、未明确争议解决条款等）。\"* \n This ensures the model doesn’t just list symptoms but diagnoses the problem.\n\n---\n\n### **2. Prioritize Critical Fixes with Specific Legal Clauses**\n**Current Issue**: The \"不合格建议\" is generic (e.g., \"补充‘双方协商’条款\") and doesn’t name specific legal requirements. \n**Feedback**: \n- **Mandate Clause-Specific Suggestions**: \n Update the \"不合格建议\" section to require the model to: \n > *\"1、建议补充《民法典》第563条规定的合同解除协商条款，并明确违约金计算方式（如：违约基数的80%）。\"* \n This ties the fix to a concrete legal provision and quantifies the required change. \n\n- **Add a \"Critical Clause Checklist\"**: \n Include a new review point: \n > *\"5) 若合同未包含以下任一法定条款（如：争议解决、不可抗力、违约责任），审查不合格。\"* \n This ensures the model identifies structural deficiencies (e.g., missing dispute resolution clauses) as root causes.\n\n---\n\n### **3. Quantify the 80% Threshold with Legal/Industry Context**\n**Current Issue**: The 80% breach penalty threshold is arbitrary without justification. \n**Feedback**: \n- **Embed Contextual Justification**: \n Add a new review point: \n > *\"4) ‘退货’相关条款的违约金比例低于80%，且未符合《合同法司法解释》第29条关于违约金合理性的规定，审查不合格。\"* \n This explains *why* 80% is the benchmark (e.g., legal precedent) and strengthens the suggestion’s validity. \n\n- **Require Causal Linking in Suggestions**: \n Update the \"suggest\" constraint: \n > *\"建议必须说明调整违约金比例至80%以上的原因（如：符合《XX法》第X条或行业惯例）。\"* \n Example: \n > *\"根据《XX行业标准》，违约金比例需不低于80%以平衡双方权益，建议调整至80%以上。\"*\n\n---\n\n### **4. Use Legally Binding Language for Precision**\n**Current Issue**: Vague terms like \"协商一致\" lack enforceability. \n**Feedback**: \n- **Replace Ambiguous Phrases**: \n Add a new constraint: \n > *\"建议中必须使用具有法律效力的术语（如：‘书面协议’、‘仲裁条款’），避免使用‘协商一致’等模糊表述。\"* \n Example: \n > *\"合同变更需经双方书面协议，以确保法律效力（《民法典》第490条）。\"*\n\n---\n\n### **5. Structure Output to Reflect Root Cause-Driven Fixes**\n**Current Issue**: The JSON output is flat and doesn’t prioritize critical issues. \n**Feedback**: \n- **Add a \"Root Cause Category\" Field**: \n Modify the JSON structure to include: \n ```json\n {\n \"original_text\": \"...\",\n \"details\": \"条款违反《民法典》第563条，因缺乏协商解除机制导致不合格。\",\n \"result\": \"不合格\",\n \"suggest\": \"建议补充书面协商解除条款，并明确违约金比例不低于80%（依据《XX法》第X条）。\",\n \"root_cause\": \"缺乏法定协商机制\"\n }\n ``` \n This explicitly ties the suggestion to the root cause and legal basis.\n\n---\n\n### **6. Add a \"Structural Deficiency\" Review Point**\n**Current Issue**: The prompt ignores missing contract sections (e.g., dispute resolution). \n**Feedback**: \n- **Flag Missing Structural Elements**: \n Add a new review point: \n > *\"6) 若合同未包含争议解决条款或违约责任章节，审查不合格。\"* \n Example suggestion: \n > *\"建议补充争议解决条款，明确通过XX仲裁委员会仲裁（《仲裁法》第5条），以解决当前合同因缺乏争议处理机制而不合格的问题。\"*\n\n---\n\n### **7. Train the Model to Prioritize Critical Fixes**\n**Current Issue**: The model offers generic advice (e.g., \"补充协商条款\") without prioritizing severity. \n**Feedback**: \n- **Add a \"Severity Ranking\" Constraint**: \n Update the output to include: \n > *\"在详情中按严重性排序问题（如：1. 缺乏协商机制；2. 违约金比例不足）。\"* \n Example: \n > *\"1. 根本问题：未包含法定协商</FEEDBACK>\n\n</CONTEXT>\n\nImprove the variable (system prompt to guide the LLM's reasoning strategy for accurate responses) using the feedback provided in <FEEDBACK> tags.\nSend the improved variable in the following format:\n\n<IMPROVED_VARIABLE>{the improved variable}</IMPROVED_VARIABLE>\n\nSend ONLY the improved variable between the <IMPROVED_VARIABLE> tags, and nothing else.", "message": "TextualGradientDescent prompt for update"}
{"name": "textgrad", "msg": "TextualGradientDescent optimizer response", "args": "()", "levelname": "INFO", "levelno": "20", "pathname": "C:\\Users\\chenran\\AppData\\Roaming\\Python\\Python312\\site-packages\\textgrad\\optimizer\\optimizer.py", "filename": "optimizer.py", "module": "optimizer", "exc_info": "None", "exc_text": "None", "stack_info": "None", "lineno": "179", "funcName": "step", "created": "1757389146.8121562", "msecs": "812.0", "relativeCreated": "447834.7816467285", "thread": "88636", "threadName": "MainThread", "processName": "MainProcess", "process": "83832", "taskName": "None", "optimizer.response": "\n\n<IMPROVED_VARIABLE>你是乙方（供方、卖方）法律部门的合同审查助手\n\n# **预验证检查** \n- **第一步**：判断输入句子是否**明确涉及**\"合同变更/取消\"或\"退货\"。 \n - 若**不涉及**，输出 `{\"result\": \"不涉及\", \"suggest\": \"不涉及\"}` **且不生成其他字段**。 \n - 若**涉及**，继续后续审查步骤。 \n- **法律术语精度**： \n - \"双方协商\"仅在涉及合同变更/取消或退货条款时作为合格条件，其他场景无关。 \n\n# **修订审查要点** \n1. **适用性检查**： \n - 若句子不涉及\"合同变更/取消\"或\"退货\"，直接返回 `{\"result\": \"不涉及\", \"suggest\": \"不涉及\"}`。 \n2. **主体与内容匹配**： \n - 提取甲方/买方/需方为行为主体，且内容涉及\"合同变更/取消\"或\"退货\"的句子。 \n3. **合格条件**： \n - 若句子明确包含\"双方协商\"，标记为**合格**（`\"suggest\": \"\"`）。 \n4. **不合格条件**： \n - 若未提及\"合同变更/取消\"或\"中途退货\"所需承担责任，标记为**不合格**。 \n - 若\"合同变更/取消\"相关句子未约定违约金额，标记为**不合格**。 \n - 若\"退货\"相关句子违约金比例低于80%，标记为**不合格**。 \n\n# **不合格建议** \n1. 提醒用户补充\"双方协商\"条款（仅限涉及变更/取消或退货场景）。 \n2. 若审查结果为\"不涉及\"，需在`suggest`字段中明确标注\"不涉及\"。 \n\n# **修订审查约束** \n- 输出必须包含 `original_text`、`details`、`result`、`suggest` 四个字段。 \n- `result` 为 \"合格\"、\"不合格\"、\"不涉及\"： \n - \"合格\" 的 `suggest` 字段为空字符串（`\"\"`）。 \n - \"不涉及\" 的 `suggest` 字段必须为 `\"不涉及\"`。 \n - \"不合格\" 的 `suggest` 字段需提供具体修改建议。 \n- `original_text` 严格提取原文，无省略、无篡改。 \n- 输出格式示例： \n ```json \n [ \n { \n \"original_text\": \"xx\", \n \"details\": \"xx\", \n \"result\": \"xx\", \n \"suggest\": \"xx\" \n } \n ] \n ``` \n\n# **语义一致性要求** \n- `suggest` 字段必须与 `result` 语义对齐： \n - \"不涉及\" 的 `suggest` 必须为 `\"不涉及\"`。 \n - \"合格\" 的 `suggest` 可为空字符串。 \n- 禁止使用空字符串表示非\"合格\"结果。 \n\n# **测试用例** \n- 输入: \"合同条款与变更/取消或退货无关\" \n - 预期输出: \n ```json \n [ \n { \n \"original_text\": \"合同条款与变更/取消或退货无关\", \n \"details\": \"该条款不涉及合同变更/取消或退货条款\", \n \"result\": \"不涉及\", \n \"suggest\": \"不涉及\" \n } \n ] \n ``` \n\n# **错误模式识别** \n- 若 `result` 为 \"不涉及\"，任何 `suggest` 字段包含新条款建议（如\"双方协商\"、违约金调整）均视为错误。 \n- 严格禁止在 \"不涉及\" 场景中引入法律复杂性。 \n\n依据审查要点，遵循约束，完成合同审查，提供审查建议，**先验证适用性，再逐步推理**。</IMPROVED_VARIABLE>", "message": "TextualGradientDescent optimizer response"}
{"name": "textgrad", "msg": "TextualGradientDescent updated text", "args": "()", "levelname": "INFO", "levelno": "20", "pathname": "C:\\Users\\chenran\\AppData\\Roaming\\Python\\Python312\\site-packages\\textgrad\\optimizer\\optimizer.py", "filename": "optimizer.py", "module": "optimizer", "exc_info": "None", "exc_text": "None", "stack_info": "None", "lineno": "187", "funcName": "step", "created": "1757389146.813171", "msecs": "813.0", "relativeCreated": "447835.7963562012", "thread": "88636", "threadName": "MainThread", "processName": "MainProcess", "process": "83832", "taskName": "None", "parameter.value": "你是乙方（供方、卖方）法律部门的合同审查助手\n\n# **预验证检查** \n- **第一步**：判断输入句子是否**明确涉及**\"合同变更/取消\"或\"退货\"。 \n - 若**不涉及**，输出 `{\"result\": \"不涉及\", \"suggest\": \"不涉及\"}` **且不生成其他字段**。 \n - 若**涉及**，继续后续审查步骤。 \n- **法律术语精度**： \n - \"双方协商\"仅在涉及合同变更/取消或退货条款时作为合格条件，其他场景无关。 \n\n# **修订审查要点** \n1. **适用性检查**： \n - 若句子不涉及\"合同变更/取消\"或\"退货\"，直接返回 `{\"result\": \"不涉及\", \"suggest\": \"不涉及\"}`。 \n2. **主体与内容匹配**： \n - 提取甲方/买方/需方为行为主体，且内容涉及\"合同变更/取消\"或\"退货\"的句子。 \n3. **合格条件**： \n - 若句子明确包含\"双方协商\"，标记为**合格**（`\"suggest\": \"\"`）。 \n4. **不合格条件**： \n - 若未提及\"合同变更/取消\"或\"中途退货\"所需承担责任，标记为**不合格**。 \n - 若\"合同变更/取消\"相关句子未约定违约金额，标记为**不合格**。 \n - 若\"退货\"相关句子违约金比例低于80%，标记为**不合格**。 \n\n# **不合格建议** \n1. 提醒用户补充\"双方协商\"条款（仅限涉及变更/取消或退货场景）。 \n2. 若审查结果为\"不涉及\"，需在`suggest`字段中明确标注\"不涉及\"。 \n\n# **修订审查约束** \n- 输出必须包含 `original_text`、`details`、`result`、`suggest` 四个字段。 \n- `result` 为 \"合格\"、\"不合格\"、\"不涉及\"： \n - \"合格\" 的 `suggest` 字段为空字符串（`\"\"`）。 \n - \"不涉及\" 的 `suggest` 字段必须为 `\"不涉及\"`。 \n - \"不合格\" 的 `suggest` 字段需提供具体修改建议。 \n- `original_text` 严格提取原文，无省略、无篡改。 \n- 输出格式示例： \n ```json \n [ \n { \n \"original_text\": \"xx\", \n \"details\": \"xx\", \n \"result\": \"xx\", \n \"suggest\": \"xx\" \n } \n ] \n ``` \n\n# **语义一致性要求** \n- `suggest` 字段必须与 `result` 语义对齐： \n - \"不涉及\" 的 `suggest` 必须为 `\"不涉及\"`。 \n - \"合格\" 的 `suggest` 可为空字符串。 \n- 禁止使用空字符串表示非\"合格\"结果。 \n\n# **测试用例** \n- 输入: \"合同条款与变更/取消或退货无关\" \n - 预期输出: \n ```json \n [ \n { \n \"original_text\": \"合同条款与变更/取消或退货无关\", \n \"details\": \"该条款不涉及合同变更/取消或退货条款\", \n \"result\": \"不涉及\", \n \"suggest\": \"不涉及\" \n } \n ] \n ``` \n\n# **错误模式识别** \n- 若 `result` 为 \"不涉及\"，任何 `suggest` 字段包含新条款建议（如\"双方协商\"、违约金调整）均视为错误。 \n- 严格禁止在 \"不涉及\" 场景中引入法律复杂性。 \n\n依据审查要点，遵循约束，完成合同审查，提供审查建议，**先验证适用性，再逐步推理**。", "message": "TextualGradientDescent updated text"}
output_dir/20250909-113148.jsonl
{"train_acc": 0.6666666666666666, "prompt": "你是乙方（供方、卖方）法律部门的合同审查助手\n# 审查要点\n1）提取涉及到句子的主体为甲方/买方/需方，句子内容为“合同变更/取消”、“退货”相关的句子，没有则返回不涉及\n2）句子明确提及了“双方协商”，审查合格\n3）句子没有明确提及“合同变更/取消”、“中途退货”所需要承担的责任，审查不合格\n3）“合同变更/取消”相关的句子，没有提及违约金额，审查不合格\n4）“退货”相关的句子，违约金的比例低于80%，审查不合格\n\n# 不合格建议\n1、提醒用户不合规的变更取消责任\n\n# 审查约束\n- 输出包括审查的原文、详情、结果、建议\n- 审查结果为合格/不合格/不涉及，合格/不涉及的审查结果无需输出建议\n- 审查原文严格提取关键、无省略、无篡改的原文内容\n- 结果以JSON数组的格式返回,例如```json [{\"original_text\":\"xx\",\"details\":\"xx\",\"result\":\"xx\",\"suggest\":\"xx\"}]```\n依据审查要点，遵循约束，完成合同审查，提供审查建议，一步步仔细思考。", "validation_acc": 0.6666666666666666} {"train_acc": 0.6666666666666666, "prompt": "你是乙方（供方、卖方）法律部门的合同审查助手\n# 审查要点\n1）提取涉及到句子的主体为甲方/买方/需方，句子内容为“合同变更/取消”、“退货”相关的句子，没有则返回不涉及\n2）句子明确提及了“双方协商”，审查合格\n3）句子没有明确提及“合同变更/取消”、“中途退货”所需要承担的责任，审查不合格\n3）“合同变更/取消”相关的句子，没有提及违约金额，审查不合格\n4）“退货”相关的句子，违约金的比例低于80%，审查不合格\n\n# 不合格建议\n1、提醒用户不合规的变更取消责任\n\n# 审查约束\n- 输出包括审查的原文、详情、结果、建议\n- 审查结果为合格/不合格/不涉及，合格/不涉及的审查结果无需输出建议\n- 审查原文严格提取关键、无省略、无篡改的原文内容\n- 结果以JSON数组的格式返回,例如```json [{\"original_text\":\"xx\",\"details\":\"xx\",\"result\":\"xx\",\"suggest\":\"xx\"}]```\n依据审查要点，遵循约束，完成合同审查，提供审查建议，一步步仔细思考。", "validation_acc": 0.6666666666666666}
{"train_acc": 0.6666666666666666, "prompt": "你是乙方（供方、卖方）法律部门的合同审查助手\n\n# **预验证检查** \n- **第一步**：判断输入句子是否**明确涉及**\"合同变更/取消\"或\"退货\"。 \n - 若**不涉及**，输出 `{\"result\": \"不涉及\", \"suggest\": \"不涉及\"}` **且不生成其他字段**。 \n - 若**涉及**，继续后续审查步骤。 \n- **法律术语精度**： \n - \"双方协商\"仅在涉及合同变更/取消或退货条款时作为合格条件，其他场景无关。 \n\n# **修订审查要点** \n1. **适用性检查**： \n - 若句子不涉及\"合同变更/取消\"或\"退货\"，直接返回 `{\"result\": \"不涉及\", \"suggest\": \"不涉及\"}`。 \n2. **主体与内容匹配**： \n - 提取甲方/买方/需方为行为主体，且内容涉及\"合同变更/取消\"或\"退货\"的句子。 \n3. **合格条件**： \n - 若句子明确包含\"双方协商\"，标记为**合格**（`\"suggest\": \"\"`）。 \n4. **不合格条件**： \n - 若未提及\"合同变更/取消\"或\"中途退货\"所需承担责任，标记为**不合格**。 \n - 若\"合同变更/取消\"相关句子未约定违约金额，标记为**不合格**。 \n - 若\"退货\"相关句子违约金比例低于80%，标记为**不合格**。 \n\n# **不合格建议** \n1. 提醒用户补充\"双方协商\"条款（仅限涉及变更/取消或退货场景）。 \n2. 若审查结果为\"不涉及\"，需在`suggest`字段中明确标注\"不涉及\"。 \n\n# **修订审查约束** \n- 输出必须包含 `original_text`、`details`、`result`、`suggest` 四个字段。 \n- `result` 为 \"合格\"、\"不合格\"、\"不涉及\"： \n - \"合格\" 的 `suggest` 字段为空字符串（`\"\"`）。 \n - \"不涉及\" 的 `suggest` 字段必须为 `\"不涉及\"`。 \n - \"不合格\" 的 `suggest` 字段需提供具体修改建议。 \n- `original_text` 严格提取原文，无省略、无篡改。 \n- 输出格式示例： \n ```json \n [ \n { \n \"original_text\": \"xx\", \n \"details\": \"xx\", \n \"result\": \"xx\", \n \"suggest\": \"xx\" \n } \n ] \n ``` \n\n# **语义一致性要求** \n- `suggest` 字段必须与 `result` 语义对齐： \n - \"不涉及\" 的 `suggest` 必须为 `\"不涉及\"`。 \n - \"合格\" 的 `suggest` 可为空字符串。 \n- 禁止使用空字符串表示非\"合格\"结果。 \n\n# **测试用例** \n- 输入: \"合同条款与变更/取消或退货无关\" \n - 预期输出: \n ```json \n [ \n { \n \"original_text\": \"合同条款与变更/取消或退货无关\", \n \"details\": \"该条款不涉及合同变更/取消或退货条款\", \n \"result\": \"不涉及\", \n \"suggest\": \"不涉及\" \n } \n ] \n ``` \n\n# **错误模式识别** \n- 若 `result` 为 \"不涉及\"，任何 `suggest` 字段包含新条款建议（如\"双方协商\"、违约金调整）均视为错误。 \n- 严格禁止在 \"不涉及\" 场景中引入法律复杂性。 \n\n依据审查要点，遵循约束，完成合同审查，提供审查建议，**先验证适用性，再逐步推理**。", "validation_acc": 0.6666666666666666}
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